Achieving Distributed Convex Optimization Within Prescribed Time for High-Order Nonlinear Multiagent Systems

Abstract

This article addresses the distributed prescribed-time convex optimization (DPTCO) problem for high-order nonlinear multiagent systems (MASs) under undirected connected graphs. A cascade design framework is proposed that divides the DPTCO implementation into distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO problem is then transformed into the prescribed-time stabilization problem of a cascaded system. Using changing Lyapunov functions and time-varying state transformations with sufficient conditions, we establish criteria for prescribed-time stabilization and prove the boundedness of internal signals in closed-loop MASs. The framework addresses robust DPTCO for chain-integrator MASs with disturbances through the introduction of novel sliding-mode variables and time-varying gains. It also solves adaptive DPTCO for strict-feedback MASs with parameter uncertainty via backstepping method and descending power state transformation. Two numerical examples verify the theoretical results.